Research workbench / 02
Clock versus function.
Bring the measurements and their provenance together. See what is known, what conflicts, and what the next study needs to resolve.
Start with evidence or your records.
Published aggregates preserve reported summaries. Synthetic examples are invented demonstrations. Neither reconstructs missing sample data.
Flexible headers. Map your columns before applying them.
Audit recalculated on import. Imported text is never treated as executable content.
Paste or edit CSV
Keep the biological units separate.
Review fields in groups. Blank values remain unknown. A row or reported n never becomes an independent donor.
Find the next useful question.
Carry the case forward.
The complete package contains your records, recalculated audit, source references, unresolved questions and any attached study brief or illustrative model.
CSV protects against spreadsheet formula prefixes. JSON retains original text. No biological age is estimated from arbitrary inputs, and no statistical significance or rejuvenation score is produced.
Data dictionary & interpretation
Hierarchy: donor_id, batch_id, sample_id, replicate_level and reported_n are separate fields. Unknown hierarchy prevents treating observations as independent.
Molecular: clock, clock_version, clock_value, clock_unit, molecular_outcome and method_status describe supplied results. Method names are not inferred.
Function: functional_endpoint names the measured job; functional_value and functional_unit hold a supplied numeric measurement. functional_outcome preserves descriptive findings. identity_outcome and safety_outcome remain separate.
Context: data_kind, source, condition, time, time_unit and notes. Time is elapsed study time, never donor age. Plots require matching clock methods, units, functional endpoints and data kind. They do not fit relationships or establish causation.